Federated Graph Learning enables multiple clients to collaboratively train graph models while protecting local private data. However, most studies have assumed that all clients contribute data voluntarily and actively. Without reasonable incentives, clients are often reluctant to contribute personal data for model training. Furthermore, the budget for incentives is limited, and if clients with low-quality graph data are incentivized to participate in training, it will negatively impact the training performance of all parties in the system. To address this, we propose AEFGL, a Reverse Auction and Value Evaluation-Based Incentive Mechanism for Federated Graph Learning. First, we design a reverse auction mechanism combining graph structural attribute motifs with client production value. Then, we propose a method for evaluating client production value based on the comparison of the client's expected reward and actual value. This mechanism can incentivize clients with high-quality graph data to participate in training within budget constraints, thereby improving the model quality. Experimental results validate the superiority of the AEFGL mechanism and the economic properties it satisfies.